Monitoring Variations in Soil Moisture and Surface Water Extent using Spire GNSS Reflection Observations

GNSS reflectometry (GNSS-R) repurposes navigation satellite transmissions as a source of illumination over the earth surface for passive bistatic radar. It has emerged as an effective tool for monitoring variations in soil moisture (SM) within the top layer (i.e., ~5 cm) of soil and ground surface water extent (SWE) in the last decade. Changes in SM and occurrence/drainage of surface water regulates the reflector’s dielectric constant and then affects reflectivity of GNSS signals. Therefore, GNSS-R-based reflectivity can be used to invert SM and SWE.

Spire Global Inc. has launched a series of cubesats that are designed to collect reflection signals from multiple GNSS systems. These satellites are deployed at low-inclination and near-polar orbits, achieving a global sampling of the earth surface. Spire receivers generate reflectivity observations at 2 Hz. These reflectivity measurements have been calibrated for antenna gain, signal propagation loss, and instrument biases.

This study evaluates Spire reflectivity data from late January to early July 2024 to map SM and SWE, under the NOAA Commercial Data Program (CDP) Ocean Surface Wind (OSW) pilot project. We initially evaluated the land coverage ratio of reflection data. After quality control, the reflection data can cover around 30% of the land gridded at 36 km daily. Reflectivity observations from different GNSS systems and receivers are generally consistent, as assessed with the data at a site in Sahara Desert with relatively homogeneous surface conditions.

For SM, Spire reflectivity observations are first corrected for surface roughness and vegetation absorption. Gridded maps of daily averaged reflectivity observations at 36 km are then generated and fitted to the corresponding SMAP soil moisture to derive the best linear fit for each grid, under the assumption that SM is generally proportional to reflectivity. Spire soil moisture measurements are then obtained applying the fit models to the daily averaged reflectivity records. Compared with daily SMAP data, the overall root mean square difference is 0.05 cm3/cm3, which is comparable to those in previous studies either using CYGNSS or Spire data.

Calm surface water facilitates more coherent reflections and higher reflectivity, compared with the rough ground surface. We used the Spire reflectivity data to delineate the recession process of a soil inundation event in Tanzania from late Jan to mid-Jun 2024. The magnitude of reflectivity was reduced with the drainage of the inundated area. The GNSS-R-based process is consistent with that revealed by Sentinel-2 optical images.

This study demonstrates the capability of Spire reflection data to monitor SM and SWE. Integrated with other GNSS-R mission data, Spire observations can fill a gap between SAR images (with high spatial resolution but long revisit time) and radiometric observations (with coarse spatial resolution and short revisit time), attributed to a bunch of small satellite collecting a myriad volume of reflection data continuously.